Relevant bibliographies by topics / Response Time

Academic literature on the topic 'Response Time'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Response Time"

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LEMESHKO, Andrii, Аrtem ANTONENKO, Oleksii DOBROVOLSKYI, and Viacheslav ZAVADSKYI. "APACHE WEB SERVER RESPONSE TIME OPTIMIZATION." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (April 27, 2023): 189–201. http://dx.doi.org/10.31891/2307-5732-2023-319-1-189-201.

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This article explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytical methods, we show that MaxClients has a concave-upward effect on response time, and hence hill-climbing techniques can be used to determine the optimal value of MaxClients. We investigate two optimizers that use hill climbing—one based on Newton’s method and one based on fuzzy control. A third technique is a heuristic that exploits the relationship between bottleneck utilization and response time minimization. In all cases, online optimization reduces the response time by a factor of 10 or more compared to the static default value. The trade-offs between online schemes are as follows. Newton’s method is well known but does not provide consistent results for highly variable data such as response times. Fuzzy control is more reliable but converges slowly. The heuristic works well in our prototype system, but it can be difficult to generalize because it requires knowledge of resource bottlenecks and the ability to measure their use. This topic is relevant in the context of a rapidly growing number of web applications and high requirements for the performance and efficiency of web servers. This topic explores how to improve the speed and response time of the Apache web server using various techniques and settings, such as optimizing server settings, using caching, data compression, optimizing request routing, and more. The purpose of this research is to improve the performance and response speed of the Apache web server, which can be useful for developers and administrators of web applications and services. The speed and response time of web servers are critical factors in meeting user needs and achieving business goals for web applications and services. Apache is one of the most widely used web servers in the world, so optimizing the response time of the Apache server is an important task for many developers and administrators of web applications and services. This study will examine various approaches and techniques for optimizing the response time of the Apache web server, including configuring server parameters, using caching, data compression, optimizing request routing, and others. The results of the study can be useful for developers and administrators of web applications and services that deal with the use of the Apache web server. Optimizing Apache server response time can significantly improve the performance and efficiency of web applications and services, which in turn can lead to user satisfaction and business goals.
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Bauer, Connie L. "Direct response advertising:Forecasting responses over time." Journal of Direct Marketing 1, no. 4 (1987): 38–50. http://dx.doi.org/10.1002/dir.4000010407.

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Zeng, Xinyu, and Olha Lysa. "RESPONSE TIME IN INERTIAL MEASUREMENT UNIT CONTROL ALGORITHMS." Measuring Equipment and Metrology 85, no. 2 (2024): 5–8. http://dx.doi.org/10.23939/istcmtm2024.02.005.

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The Inertial Measurement Unit (IMU) [1] is a cornerstone technology in various fields, ranging from aerospace to consumer electronics, where accurate motion tracking is paramount. Central to the effectiveness of an IMU is the quality of data processing, particularly in the context of filtering techniques. This study compares two filtering methods: Complementary Filters and Kalman Filters, in their application to IMU data processing. Complementary Filters, known for their simplicity and efficiency, contrast with the more complex but potentially more accurate Kalman Filters. Our investigation delves into the underpinnings of each filter, followed by a practical analysis of their performance in real-world IMU applications. We comprehensively compare these filters in terms of accuracy, computational efficiency, and ease of implementation. This research offers valuable insights for practitioners and researchers in selecting the most suitable filtering approach for specific IMU-based applications, enhancing the overall quality of motion sensing and analysis.
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Zhu, Ming, Xiaoliu Cui, and Guodong Fan. "Modeling and Verification of Response Time of QoS-aware Web Service Composition by Timed CSP." Journal of Ubiquitous Systems and Pervasive Networks 11, no. 1 (May 1, 2019): 01–09. http://dx.doi.org/10.5383/juspn.11.01.001.

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Kodratoff, Y., and J. A. Campbell. "Response time." Artificial Intelligence Review 2, no. 1 (1988): 1–2. http://dx.doi.org/10.1007/bf00139622.

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Deanna K. Kreisel. "Response: Queering Time." Victorian Studies 60, no. 2 (2018): 236. http://dx.doi.org/10.2979/victorianstudies.60.2.09.

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Corominas, Albert, Wieslaw Kubiak, and Natalia Moreno Palli. "Response time variability." Journal of Scheduling 10, no. 2 (January 25, 2007): 97–110. http://dx.doi.org/10.1007/s10951-006-0002-8.

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Silverman, R. A. ""Vertical Response Time": The Hidden Component of Ambulance Response Time." Academic Emergency Medicine 12, Supplement 1 (May 1, 2005): 142. http://dx.doi.org/10.1197/j.aem.2005.03.399.

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Scherbaum, Stefan, and Maja Dshemuchadse. "Higher response time increases theta energy, conflict increases response time." Clinical Neurophysiology 124, no. 7 (July 2013): 1477–79. http://dx.doi.org/10.1016/j.clinph.2012.12.007.

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Vuorinen, Mari, Pentti Varpasuo, and Jukka Kahkonen. "ICONE19-43207 Reaction-time response of a large commercial aircraft." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_81.

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Dissertations / Theses on the topic "Response Time"

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Liu, Binzhang M. S. "Characterizing Web Response Time." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36741.

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It is critical to understand WWW latency in order to design better HTTP protocols. In this study we characterize Web response time and examine the effects of proxy caching, network bandwidth, traffic load, persistent connections for a page, and periodicity. Based on studies with four workloads, we show that at least a quarter of the total elapsed time is spent on establishing TCP connections with HTTP/1.0. The distributions of connection time and elapsed time can be modeled using Pearson, Weibul, or Log-logistic distributions. We also characterize the effect of a user's network bandwidth on response time. Average connection time from a client via a 33.6 K modem is two times longer than that from a client via switched Ethernet. We estimate the elapsed time savings from using persistent connections for a page to vary from about a quarter to a half. Response times display strong daily and weekly patterns. This study finds that a proxy caching server is sensitive to traffic loads. Contrary to the typical thought about Web proxy caching, this study also finds that a single stand-alone squid proxy cache does not always reduce response time for our workloads. Implications of these results to future versions of the HTTP protocol and to Web application design also are discussed.
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Vaughn, Chad Dean. "Light in response to time /." Cincinnati, Ohio : University of Cincinnati, 2008. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1212179576.

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Thesis (Master of Architecture)--University of Cincinnati, 2008.
Committee/Advisors: Michael McInturf (Committee Chair), Elizabeth Riorden (Committee Co-Chair). Title from electronic theses title page (viewed Sep. 2, 2008). Includes abstract. Keywords: Light. Includes bibliographical references.
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Andersson, Karl Robert. "Dose-response-time data analysis." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/100470/.

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The traditional approach to pharmacodynamic modelling relies on knowledge about the pharmacokinetics. A prerequisite for obtaining kinetic information is reliable exposure data. However, in several therapeutic areas, exposure data are unavailable including when the drug response precedes the systemic exposure (for example pulmonary drug administration) and when the drug is locally administered (for example ophthalmics). Dose-response-time (DRT) data analysis provides an alternative to exposure-driven pharmacodynamic modelling when exposure data are sparse or lacking. In DRT modelling, the response data are assumed to contain enough information about the drug kinetics, whereby a biophase model can be developed and act as the driver of the pharmacological response. The following work presents the fundamental principles of DRT modelling. This include the entire procedure of identifying a DRT model, encompassing the assessment of the biophase function and the pharmacodynamic model, extensions to cover population variations, identifiability analysis, parameter estimation, and model validation. To demonstrate the utility of the technique, two extensive pre-clinical DRT studies of the interaction between nicotinic acid (NiAc) and free fatty acids (FFA) are presented. The first study covered the response behaviour following intravenous and oral NiAc dosing in both normal (lean) and diseased (obese) rats. The second study extended the models of the first study to incorporate insulin as a driver of the FFA response. Moreover, data from chronic trials were analysed with the aim to quantitatively understand the adaptive behaviours associated with long-term NiAc treatments. The aim of this work is to answer the questions of when and how to use DRT data analysis, and what the limitations of the method are. The DRT models of the first study were successfully fitted to all response-time courses in lean rats, with high precision in the parameter estimates (relative standard errors (RSE) < 25%), visual predictive check (VPC) and individual plots that captured the population and subject trends, and "-shrinkages of less than 10%. The model for the obese rats were less precise, with specific parameters being practically non-identifiable (with, for example, RSE 250%). The results for both lean and obese rats were generally consistent with those of an exposure-driven reference model, albeit with less precision and accuracy in the parameter estimates. Finally, the model was able to describe non-linear biophase kinetics, present at high oral dosages of NiAc. The DRT models of the second study were able to capture the response-time courses for insulin and FFA on a population and individual level, and for both lean and obese rats. However, many parameters were uncertain (with RSE of, for example, 30-50%) and some were practically non-identifiable (with RSE of > 100%). The estimates were generally less precise and more inaccurate than those obtained in an exposure-driven reference model. Yet, most parameter estimates of the DRT models were within one standard deviation from those of the exposure-driven model. The final model was used to predict steady-state FFA exposures following repeated NiAc dosing for a range of different infusion protocols. The optimal dosing regimens consisted of infusions and wash-out periods were the wash-outs were 2h longer than the infusions. These predictions were consistent with those made by the exposure-driven model. Albeit, the DRT model predicted a slightly lower optimal reduction of FFA exposure. It is important to recognise that DRT analyses introduce bias and variability in the parameter estimates. To obtain reliable results, it is advisable to have rich pharmacodynamic data, covering drug administration at different routes, rates, and schedules. With these issues taken into account, the technique still performed well in the two extensive studies presented in this work. In conclusion, DRT data analysis is a modelling technique used in situations when exposure data are unavailable. The method is versatile and can describe a range of different pharmacological behaviours. Precision and accuracy is lost when comparing to an exposure driven pharmacodynamic modelling approach. Thus, DRT modelling is not to be considered as a replacement of the gold-standard pharmacokinetic-pharmacodynamic framework, but rather as a compliment when exposure data are unavailable.
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VAUGHN, CHAD DEAN. "Light: in response to time." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1212179576.

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Stechly, Seivertson Tracie L. "Response time to batted balls." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=1195.

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Thesis (M.S.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains vii, 52 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 36-39).
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Guynes, Jan L. (Jan Lucille). "Impacts of Personality Type and Computer System Response Time on Anxiety and User Response Time." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc330969/.

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The purpose of this research was to determine if personality type and system response time have any effect on state anxiety and user response time. The sample for this study consisted of senior and graduate level college students who possessed basic know 1 edge of a text editor. Each test subject was administered the Jenkins Activity Survey to determine scores for Type A versus Type B, speed and impatience, involvement, and competitiveness. The test subjects were randomly assigned to one of three treatment groups (good, variable, and poor system response time). They were required to edit a text file which contained multiple errors. The test subjects were provided hard copies of the file with errors (errors highlighted) and the file as should appear without the errors. The test situation for each test subject was identical, except for changes in system response time. The A-state scale of the State-Trait Anxiety Inventory (STAI) was administered to the test subjects immediately prior to the edit task in order to determine pre-task state anxiety levels. The A-state scale of the STAI was again administered immediately after the edit task in order to determine post-task state anxiety levels. Analysis of variance, analysis of covariance, regression, and two sample t-tests were used to analyze the data collected. All hypotheses were tested at the alpha .05 level. The most significant finding of this study was the positive relationship between state anxiety and system response time. It was originally predicted that the Type A personality would experience a greater increase in state anxiety than the Type B personality. However, that was not found to be true. Both Type A and Type B individuals experience an increase in state anxiety during periods of poor or variable system response time. This study also confirms prior research regarding user and system response time. There is a significant positive relationship between user response time and system response time. Personality type, specifically the Type A personality, contributes toward this relationship.
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Feld, Timo [Verfasser]. "Response time analyses of adaptive variable-rate-tasks / Timo Feld." Ulm : Universität Ulm, 2020. http://d-nb.info/1203716273/34.

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Stupak, Noah. "Time-delays and system response times in human-computer interaction /." Online version of thesis, 2009. http://hdl.handle.net/1850/10867.

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Rosengren, Robin. "CAN ANALYZATOR : Worst case response time." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-38692.

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McRorie, Margaret. "Response time and general mental ability." Thesis, Queen's University Belfast, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.483465.

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Books on the topic "Response Time"

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Potter, Simon M. Nonlinear impulse response functions. [New York, N.Y.]: Federal Reserve Bank of New York, 1999.

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Schnipke, Deborah L. Representing response-time information in item banks. Newtown, PA: Law School Admission Council, 1999.

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G, Dabija Vlad, ed. Planning for real time event response management. Upper Saddle River, NJ: Prentice Hall PTR, 2000.

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Services, Ontario Ministry of the Solicitor General and Correctional. Differential police response: A manual for the development and implementation of an alternative response system for calls for police service. Toronto, Ont: Ministry of the Solicitor General and Correctional Services, 1994.

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Hanssens, Dominique M., Leonard J. Parsons, and Randall L. Schultz. Market Response Models: Econometric and Time Series Analysis. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1073-7.

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Johnson, M. L. Ampoule failure sensor time response testing: Experiment 1. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.

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J, Parsons Leonard, and Schultz Randall L, eds. Market response models: Econometric and time series analysis. Boston: Kluwer Academic Publishers, 1990.

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A, Watring D., and George C. Marshall Space Flight Center., eds. Ampoule failure sensor time response testing: Experiment 1. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.

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United States. Bureau of Labor Statistics, ed. Part-time work: A choice or a response. [Washington, DC]: U.S. Dept. of Labor, Bureau of Labor Statistics, 1994.

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United States. Bureau of Labor Statistics, ed. Part-time work: A choice or a response. [Washington, DC]: U.S. Dept. of Labor, Bureau of Labor Statistics, 1994.

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Book chapters on the topic "Response Time"

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Kao, Ming-Yang. "Response Time." In Encyclopedia of Algorithms, 777. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_343.

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N. Jazar, Reza, and Hormoz Marzbani. "Time Response." In Vehicle Vibrations, 117–209. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43486-0_6.

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Weik, Martin H. "response time." In Computer Science and Communications Dictionary, 1483. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16255.

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Liu, Tianshu, John P. Sullivan, Keisuke Asai, Christian Klein, and Yasuhiro Egami. "Time Response." In Experimental Fluid Mechanics, 163–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68056-5_7.

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Verhaeghen, Paul. "Response Time." In Encyclopedia of Geropsychology, 1–9. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-080-3_211-1.

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Guzmán, José Luis, Ramon Costa-Castelló, Manuel Berenguel, and Sebastián Dormido. "Time Response." In Automatic Control with Interactive Tools, 53–93. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09920-5_3.

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Fox, Jean-Paul. "Response Time Item Response Models." In Bayesian Item Response Modeling, 227–54. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0742-4_8.

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Weik, Martin H. "response-time window." In Computer Science and Communications Dictionary, 1483. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16257.

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Ratcliff, Roger. "Response time distributions." In APA handbook of research methods in psychology, Vol 1: Foundations, planning, measures, and psychometrics., 429–43. Washington: American Psychological Association, 2012. http://dx.doi.org/10.1037/13619-023.

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Palani, S. "Time Response Analysis." In Automatic Control Systems, 163–326. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93445-3_2.

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Conference papers on the topic "Response Time"

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Klein, Rebecca, Matthew Smith, and David Sierkowski. "Reduce response time." In the 35th annual ACM SIGUCCS conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1294046.1294091.

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Bozhko, Sergey, Georg von der Bruggen, and Bjorn B. Brandenburg. "Monte Carlo Response-Time Analysis." In 2021 IEEE Real-Time Systems Symposium (RTSS). IEEE, 2021. http://dx.doi.org/10.1109/rtss52674.2021.00039.

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Zhang, Dan, and Luo Si. "Modeling search response time." In the 32nd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1571941.1572100.

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Armitage, David, and James O. Larimer. "Liquid crystal response time." In Photonics West '98 Electronic Imaging, edited by Ming H. Wu. SPIE, 1998. http://dx.doi.org/10.1117/12.305534.

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Olson, Paul L. "Driver Perception Response Time." In SAE International Congress and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/890731.

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Raatikainen, K. E. E. "Approximating response time distributions." In the 1989 ACM SIGMETRICS international conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/75108.75393.

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Pollex, Victor, Steffen Kollmann, and Frank Slomka. "Generalizing Response-Time Analysis." In 2010 IEEE 16th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). IEEE, 2010. http://dx.doi.org/10.1109/rtcsa.2010.36.

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Hunt, L. R., and Renjeng Su. "Output Time Response Approximation." In 1986 American Control Conference. IEEE, 1986. http://dx.doi.org/10.23919/acc.1986.4789169.

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Srour, F. Jordan, and Nabil Georges Badr. "Time Synchronization in Emergency Response Time Measurement." In 10th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006096001990204.

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Stigge, Martin, Nan Guan, and Wang Yi. "Refinement-Based Exact Response-Time Analysis." In 2014 26th Euromicro Conference on Real-Time Systems (ECRTS). IEEE, 2014. http://dx.doi.org/10.1109/ecrts.2014.29.

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Reports on the topic "Response Time"

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Gilbert, Stanley W., Thomas Cleary, Paul A. Reneke, Richard Peacock, and David Butry. Response Time Impact of Smoke Alarms. National Institute of Standards and Technology, September 2021. http://dx.doi.org/10.6028/nist.tn.2174.

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Baum, D. W., R. M. Kuklo, J. E. Reaugh, and S. C. Simonson. Time-resolved diagnostics for concrete target response. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/238554.

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Fuchs, Victor, and Joyce Jacobsen. Employee Response to Compulsory Short-Time Work. Cambridge, MA: National Bureau of Economic Research, December 1986. http://dx.doi.org/10.3386/w2089.

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Barrow, Lisa, Cecilia Elena Rouse, and Amanda McFarland. Who Has the Time? Community College Students’ Time-Use Response to Financial Incentives. Federal Reserve Bank of Chicago, 2020. http://dx.doi.org/10.21033/wp-2020-03.

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Andrea, Ciani, and Mau Karsten. When Time Matters: Eastern Europe's Response to Chinese Competition. Maastricht University, Graduate School of Business and Economics, 2020. http://dx.doi.org/10.26481/umagsb.20007.

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Modine, F. A. Time domain response of electrical ceramics -- Micro to megaseconds. Office of Scientific and Technical Information (OSTI), November 1997. http://dx.doi.org/10.2172/564232.

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Nosofsky, Robert. Response-Time Approach to Contrasting Models of Perceptual Classification. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ada581133.

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Hancock, Peter A., and Jong W. Kim. Cognitive Modeling of Performance Response Capacity Under Time Pressure. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada547369.

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Kelley, F. N., M. Morton, and D. Plazek. Time-Dependent Response and Fracture of Cross-Linked Polymer. Fort Belvoir, VA: Defense Technical Information Center, December 1986. http://dx.doi.org/10.21236/ada172047.

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Shestakov, Aleksei I. Filter frequency response of time dependent signal using Laplace transform. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1418944.

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